An observer-based adaptive neural network tracking control of robotic systems

• We make a full analysis of the robot states by applying observer-based adaptive wavelet neural network tracking control scheme.
• The recurrent adaptive wavelet neural network model is used to approximate the dynamics of the robotic system.
• We use the adaptive wavelet neural dynamics to tune the weights of the robot parameters and the bounds of the gains of delay states.
• The stability of the closed-loop system is derived from Lyapunov criterion and Riccati-inequality to achieve H∞ tracking performance.
• A three-links rolling cart is given to illustrate the effectiveness of the proposed control scheme.

Robotic systems have inherently nonlinear phenomena as joints undergo sliding and/or rotating. This in turn requires that the system running states be predicted correctly. This paper makes a full analysis of the robot states by applying observer-based adaptive wavelet neural network (OBAWNN) tracking control scheme to tackle these phenomena such as system uncertainties, multiple time-delayed state uncertainties, and external disturbances such that the closed loop system signals must obey uniform ultimate boundedness and achieve H∞ tracking performance. The recurrent adaptive wavelet neural network model is used to approximate the dynamics of the robotic system, while an observer-based adaptive control scheme is to stabilize the system. The advantage of employing adaptive wavelet neural dynamics is that we can utilize the neuron information by activation functions to on-line tune the hidden-to-output weights, and the adaptation parameters to estimate the robot parameters and the bounds of the gains of delay states directly using linear analytical results. It is shown that the stability of the closed-loop system is guaranteed by some sufficient conditions derived from Lyapunov criterion and Riccati-inequality. Finally, a numerical example of a three-links rolling cart is given to illustrate the effectiveness of the proposed control scheme.